Artificial Neural Network Based Fault Diagnosis of IC Engines

Fault diagnosis is important to avoid unforeseen failures of IC engines, but normally requires an expert to interpret analysis results. Artificial Neural Networks are potential tools for the automated fault diagnosis of IC engines, as they can learn the patterns corresponding to various faults. Most engine faults can be classified into two categories: combustion faults and mechanical faults. Misfire is a typical combustion fault; piston slap and big end bearing knock are common mechanical faults. The automated diagnostic system proposed in this paper has three main stages, each stage including three neural networks. The first stage is the fault detection stage, where the neural networks detect whether there are faults in the engine and if so which kind. In the second stage, based on the detection results, the severity of the faults was identified. In the third stage, the neural networks localize which cylinder has a fault. The critical thing for a neural network is its input feature vector, and a previous study had indicated a number of features that should differentiate between the different faults and their location, based on advanced signal processing of the vibration signals measured for different normal and fault conditions. In this study, an advanced feature selection technology was employed to select the significant features as the inputs to networks. The input vectors were separated into two groups, one for training the network, and the other for its test. Finally it has been demonstrated that the neural network based system can automatically differentiate and diagnose a number of engine faults, including location and severity.